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tasks.py
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import os
import sys
import fcntl
import datetime
import json
import re
import time
import zipfile
import threading
import hashlib
import shutil
import subprocess
import pprint
import random
from invoke import task
import boto3
import botocore.exceptions
import multiprocessing
import io
import ai2thor.build
import logging
logger = logging.getLogger()
logger.setLevel(logging.INFO)
handler = logging.StreamHandler(sys.stdout)
handler.setLevel(logging.INFO)
formatter = logging.Formatter(
"%(asctime)s [%(process)d] %(funcName)s - %(levelname)s - %(message)s"
)
handler.setFormatter(formatter)
logger.addHandler(handler)
def add_files(zipf, start_dir, exclude_ext=()):
for root, dirs, files in os.walk(start_dir):
for f in files:
fn = os.path.join(root, f)
if any(map(lambda ext: fn.endswith(ext), exclude_ext)):
#print("skipping file %s" % fn)
continue
arcname = os.path.relpath(fn, start_dir)
if arcname.split("/")[0].endswith("_BackUpThisFolder_ButDontShipItWithYourGame"):
# print("skipping %s" % arcname)
continue
# print("adding %s" % arcname)
zipf.write(fn, arcname)
def push_build(build_archive_name, zip_data, include_private_scenes):
logger.info("start of push_build")
import boto3
from base64 import b64encode
# subprocess.run("ls %s" % build_archive_name, shell=True)
# subprocess.run("gsha256sum %s" % build_archive_name)
logger.info("boto3 resource")
s3 = boto3.resource("s3", region_name="us-west-2")
acl = "public-read"
bucket = ai2thor.build.PUBLIC_S3_BUCKET
if include_private_scenes:
bucket = ai2thor.build.PRIVATE_S3_BUCKET
acl = "private"
logger.info("archive base")
archive_base = os.path.basename(build_archive_name)
key = "builds/%s" % (archive_base,)
sha256_key = "builds/%s.sha256" % (os.path.splitext(archive_base)[0],)
logger.info("hashlib sha256")
sha = hashlib.sha256(zip_data)
try:
logger.info("pushing build %s" % (key,))
s3.Object(bucket, key).put(Body=zip_data, ACL=acl, ChecksumSHA256=b64encode(sha.digest()).decode('ascii'))
logger.info("pushing sha256 %s" % (sha256_key,))
s3.Object(bucket, sha256_key).put(
Body=sha.hexdigest(), ACL=acl, ContentType="text/plain"
)
except botocore.exceptions.ClientError as e:
logger.error("caught error uploading archive %s: %s" % (build_archive_name, e))
logger.info("pushed build %s to %s" % (bucket, build_archive_name))
def _webgl_local_build_path(prefix, source_dir="builds"):
return os.path.join(
os.getcwd(), "unity/{}/thor-{}-WebGL/".format(source_dir, prefix)
)
def _unity_version():
import yaml
with open("unity/ProjectSettings/ProjectVersion.txt") as pf:
project_version = yaml.load(pf.read(), Loader=yaml.FullLoader)
return project_version["m_EditorVersion"]
def _unity_path():
unity_version = _unity_version()
standalone_path = None
if sys.platform.startswith("darwin"):
unity_hub_path = (
"/Applications/Unity/Hub/Editor/{}/Unity.app/Contents/MacOS/Unity".format(
unity_version
)
)
standalone_path = (
"/Applications/Unity-{}/Unity.app/Contents/MacOS/Unity".format(
unity_version
)
)
elif "win" in sys.platform:
unity_hub_path = "C:/PROGRA~1/Unity/Hub/Editor/{}/Editor/Unity.exe".format(
unity_version
)
# TODO: Verify windows unity standalone path
standalone_path = "C:/PROGRA~1/{}/Editor/Unity.exe".format(unity_version)
elif sys.platform.startswith("linux"):
unity_hub_path = "{}/Unity/Hub/Editor/{}/Editor/Unity".format(
os.environ["HOME"], unity_version
)
if standalone_path and os.path.exists(standalone_path):
unity_path = standalone_path
else:
unity_path = unity_hub_path
return unity_path
def _build(unity_path, arch, build_dir, build_name, env={}):
import yaml
project_path = os.path.join(os.getcwd(), unity_path)
# osxintel64 is not a BuildTarget
build_target_map = dict(OSXIntel64="OSXUniversal")
# -buildTarget must be passed as an option for the CloudRendering target otherwise a clang error
# will get thrown complaining about missing features.h
command = (
"%s -quit -batchmode -logFile %s/%s.log -projectpath %s -buildTarget %s -executeMethod Build.%s"
% (_unity_path(), os.getcwd(), build_name, project_path, build_target_map.get(arch, arch), arch)
)
target_path = os.path.join(build_dir, build_name)
full_env = os.environ.copy()
full_env.update(env)
full_env["UNITY_BUILD_NAME"] = target_path
result_code = subprocess.check_call(command, shell=True, env=full_env)
print("Exited with code {}".format(result_code))
success = result_code == 0
if success:
generate_build_metadata(os.path.join(project_path, build_dir, "metadata.json"))
return success
def generate_build_metadata(metadata_path):
# this server_types metadata is maintained
# to allow future versions of the Python API
# to launch older versions of the Unity build
# and know whether the Fifo server is available
server_types = ["WSGI"]
try:
import ai2thor.fifo_server
server_types.append("FIFO")
except Exception as e:
pass
with open(os.path.join(metadata_path), "w") as f:
f.write(json.dumps(dict(server_types=server_types)))
def class_dataset_images_for_scene(scene_name):
import ai2thor.controller
from itertools import product
from collections import defaultdict
import numpy as np
import cv2
env = ai2thor.controller.Controller(quality="Low")
player_size = 300
zoom_size = 1000
target_size = 256
rotations = [0, 90, 180, 270]
horizons = [330, 0, 30]
buffer = 15
# object must be at least 40% in view
min_size = ((target_size * 0.4) / zoom_size) * player_size
env.start(width=player_size, height=player_size)
env.reset(scene_name)
event = env.step(
dict(
action="Initialize",
gridSize=0.25,
renderInstanceSegmentation=True,
renderSemanticSegmentation=False,
renderImage=False,
)
)
for o in event.metadata["objects"]:
if o["receptacle"] and o["receptacleObjectIds"] and o["openable"]:
print("opening %s" % o["objectId"])
env.step(
dict(action="OpenObject", objectId=o["objectId"], forceAction=True)
)
event = env.step(dict(action="GetReachablePositions", gridSize=0.25))
visible_object_locations = []
for point in event.metadata["actionReturn"]:
for rot, hor in product(rotations, horizons):
exclude_colors = set(
map(tuple, np.unique(event.instance_segmentation_frame[0], axis=0))
)
exclude_colors.update(
set(
map(
tuple,
np.unique(event.instance_segmentation_frame[:, -1, :], axis=0),
)
)
)
exclude_colors.update(
set(
map(tuple, np.unique(event.instance_segmentation_frame[-1], axis=0))
)
)
exclude_colors.update(
set(
map(
tuple,
np.unique(event.instance_segmentation_frame[:, 0, :], axis=0),
)
)
)
event = env.step(
dict(
action="TeleportFull",
x=point["x"],
y=point["y"],
z=point["z"],
rotation=rot,
horizon=hor,
forceAction=True,
),
raise_for_failure=True,
)
visible_objects = []
for o in event.metadata["objects"]:
if o["visible"] and o["objectId"] and o["pickupable"]:
color = event.object_id_to_color[o["objectId"]]
mask = (
(event.instance_segmentation_frame[:, :, 0] == color[0])
& (event.instance_segmentation_frame[:, :, 1] == color[1])
& (event.instance_segmentation_frame[:, :, 2] == color[2])
)
points = np.argwhere(mask)
if len(points) > 0:
min_y = int(np.min(points[:, 0]))
max_y = int(np.max(points[:, 0]))
min_x = int(np.min(points[:, 1]))
max_x = int(np.max(points[:, 1]))
max_dim = max((max_y - min_y), (max_x - min_x))
if (
max_dim > min_size
and min_y > buffer
and min_x > buffer
and max_x < (player_size - buffer)
and max_y < (player_size - buffer)
):
visible_objects.append(
dict(
objectId=o["objectId"],
min_x=min_x,
min_y=min_y,
max_x=max_x,
max_y=max_y,
)
)
print(
"[%s] including object id %s %s"
% (scene_name, o["objectId"], max_dim)
)
if visible_objects:
visible_object_locations.append(
dict(point=point, rot=rot, hor=hor, visible_objects=visible_objects)
)
env.stop()
env = ai2thor.controller.Controller()
env.start(width=zoom_size, height=zoom_size)
env.reset(scene_name)
event = env.step(dict(action="Initialize", gridSize=0.25))
for o in event.metadata["objects"]:
if o["receptacle"] and o["receptacleObjectIds"] and o["openable"]:
print("opening %s" % o["objectId"])
env.step(
dict(action="OpenObject", objectId=o["objectId"], forceAction=True)
)
for vol in visible_object_locations:
point = vol["point"]
event = env.step(
dict(
action="TeleportFull",
x=point["x"],
y=point["y"],
z=point["z"],
rotation=vol["rot"],
horizon=vol["hor"],
forceAction=True,
),
raise_for_failure=True,
)
for v in vol["visible_objects"]:
object_id = v["objectId"]
min_y = int(round(v["min_y"] * (zoom_size / player_size)))
max_y = int(round(v["max_y"] * (zoom_size / player_size)))
max_x = int(round(v["max_x"] * (zoom_size / player_size)))
min_x = int(round(v["min_x"] * (zoom_size / player_size)))
delta_y = max_y - min_y
delta_x = max_x - min_x
scaled_target_size = max(delta_x, delta_y, target_size) + buffer * 2
if min_x > (zoom_size - max_x):
start_x = min_x - (scaled_target_size - delta_x)
end_x = max_x + buffer
else:
end_x = max_x + (scaled_target_size - delta_x)
start_x = min_x - buffer
if min_y > (zoom_size - max_y):
start_y = min_y - (scaled_target_size - delta_y)
end_y = max_y + buffer
else:
end_y = max_y + (scaled_target_size - delta_y)
start_y = min_y - buffer
# print("max x %s max y %s min x %s min y %s" % (max_x, max_y, min_x, min_y))
# print("start x %s start_y %s end_x %s end y %s" % (start_x, start_y, end_x, end_y))
print("storing %s " % object_id)
img = event.cv2img[start_y:end_y, start_x:end_x, :]
dst = cv2.resize(
img, (target_size, target_size), interpolation=cv2.INTER_LANCZOS4
)
object_type = object_id.split("|")[0].lower()
target_dir = os.path.join("images", scene_name, object_type)
h = hashlib.md5()
h.update(json.dumps(point, sort_keys=True).encode("utf8"))
h.update(json.dumps(v, sort_keys=True).encode("utf8"))
os.makedirs(target_dir, exist_ok=True)
cv2.imwrite(os.path.join(target_dir, h.hexdigest() + ".png"), dst)
env.stop()
return scene_name
@task
def build_class_dataset(context):
import concurrent.futures
import ai2thor.controller
multiprocessing.set_start_method("spawn")
controller = ai2thor.controller.Controller()
executor = concurrent.futures.ProcessPoolExecutor(max_workers=4)
futures = []
for scene in controller.scene_names():
print("processing scene %s" % scene)
futures.append(executor.submit(class_dataset_images_for_scene, scene))
for f in concurrent.futures.as_completed(futures):
scene = f.result()
print("scene name complete: %s" % scene)
def local_build_name(prefix, arch):
return "thor-%s-%s" % (prefix, arch)
@task
def local_build_test(context, prefix="local", arch="OSXIntel64"):
from ai2thor.tests.constants import TEST_SCENE
local_build(context, prefix, arch, [TEST_SCENE])
@task(iterable=["scenes"])
def local_build(
context, prefix="local", arch="OSXIntel64", scenes=None, scripts_only=False
):
import ai2thor.controller
build = ai2thor.build.Build(arch, prefix, False)
env = dict()
if os.path.isdir("unity/Assets/Private/Scenes"):
env["INCLUDE_PRIVATE_SCENES"] = "true"
build_dir = os.path.join("builds", build.name)
if scripts_only:
env["BUILD_SCRIPTS_ONLY"] = "true"
if scenes:
env["BUILD_SCENES"] = ",".join(
map(ai2thor.controller.Controller.normalize_scene, scenes)
)
if _build("unity", arch, build_dir, build.name, env=env):
print("Build Successful")
else:
print("Build Failure")
generate_quality_settings(context)
@task
def webgl_build(
context,
scenes="",
room_ranges=None,
directory="builds",
prefix="local",
verbose=False,
content_addressable=False,
crowdsource_build=False,
):
"""
Creates a WebGL build
:param context:
:param scenes: String of scenes to include in the build as a comma separated list
:param prefix: Prefix name for the build
:param content_addressable: Whether to change the unityweb build files to be content-addressable
have their content hashes as part of their names.
:return:
"""
from functools import reduce
def file_to_content_addressable(file_path, json_metadata_file_path, json_key):
# name_split = os.path.splitext(file_path)
path_split = os.path.split(file_path)
directory = path_split[0]
file_name = path_split[1]
print("File name {} ".format(file_name))
with open(file_path, "rb") as f:
h = hashlib.md5()
h.update(f.read())
md5_id = h.hexdigest()
new_file_name = "{}_{}".format(md5_id, file_name)
os.rename(file_path, os.path.join(directory, new_file_name))
with open(json_metadata_file_path, "r+") as f:
unity_json = json.load(f)
print("UNITY json {}".format(unity_json))
unity_json[json_key] = new_file_name
print("UNITY L {}".format(unity_json))
f.seek(0)
json.dump(unity_json, f, indent=4)
arch = "WebGL"
build_name = local_build_name(prefix, arch)
if room_ranges is not None:
floor_plans = [
"FloorPlan{}_physics".format(i)
for i in reduce(
lambda x, y: x + y,
map(
lambda x: x + [x[-1] + 1],
[
list(range(*tuple(int(y) for y in x.split("-"))))
for x in room_ranges.split(",")
],
),
)
]
scenes = ",".join(floor_plans)
if verbose:
print(scenes)
env = dict(BUILD_SCENES=scenes)
if crowdsource_build:
env["DEFINES"] = "CROWDSOURCE_TASK"
if _build("unity", arch, directory, build_name, env=env):
print("Build Successful")
else:
print("Build Failure")
build_path = _webgl_local_build_path(prefix, directory)
generate_quality_settings(context)
# the remainder of this is only used to generate scene metadata, but it
# is not part of building webgl player
rooms = {
"kitchens": {"name": "Kitchens", "roomRanges": range(1, 31)},
"livingRooms": {"name": "Living Rooms", "roomRanges": range(201, 231)},
"bedrooms": {"name": "Bedrooms", "roomRanges": range(301, 331)},
"bathrooms": {"name": "Bathrooms", "roomRanges": range(401, 431)},
"foyers": {"name": "Foyers", "roomRanges": range(501, 531)},
}
room_type_by_id = {}
for room_type, room_data in rooms.items():
for room_num in room_data["roomRanges"]:
room_id = "FloorPlan{}_physics".format(room_num)
room_type_by_id[room_id] = {"type": room_type, "name": room_data["name"]}
scene_metadata = {}
for scene_name in scenes.split(","):
if scene_name not in room_type_by_id:
# allows for arbitrary scenes to be included dynamically
room_type = {"type": "Other", "name": None}
else:
room_type = room_type_by_id[scene_name]
if room_type["type"] not in scene_metadata:
scene_metadata[room_type["type"]] = {
"scenes": [],
"name": room_type["name"],
}
scene_metadata[room_type["type"]]["scenes"].append(scene_name)
if verbose:
print(scene_metadata)
to_content_addressable = [
("{}.data".format(build_name), "dataUrl"),
("{}.loader.js".format(build_name), "loaderUrl"),
("{}.wasm".format(build_name), "wasmCodeUrl"),
("{}.framework.js".format(build_name), "wasmFrameworkUrl"),
]
for file_name, key in to_content_addressable:
file_to_content_addressable(
os.path.join(build_path, "Build/{}".format(file_name)),
os.path.join(build_path, "Build/{}.json".format(build_name)),
key,
)
with open(os.path.join(build_path, "scenes.json"), "w") as f:
f.write(json.dumps(scene_metadata, sort_keys=False, indent=4))
@task
def generate_quality_settings(ctx):
import yaml
class YamlUnity3dTag(yaml.SafeLoader):
def let_through(self, node):
return self.construct_mapping(node)
YamlUnity3dTag.add_constructor(
"tag:unity3d.com,2011:47", YamlUnity3dTag.let_through
)
qs = yaml.load(
open("unity/ProjectSettings/QualitySettings.asset").read(),
Loader=YamlUnity3dTag,
)
quality_settings = {}
default = "Ultra"
for i, q in enumerate(qs["QualitySettings"]["m_QualitySettings"]):
quality_settings[q["name"]] = i
assert default in quality_settings
with open("ai2thor/_quality_settings.py", "w") as f:
f.write("# GENERATED FILE - DO NOT EDIT\n")
f.write("DEFAULT_QUALITY = '%s'\n" % default)
f.write("QUALITY_SETTINGS = " + pprint.pformat(quality_settings))
def git_commit_comment():
comment = (
subprocess.check_output("git log -n 1 --format=%B", shell=True)
.decode("utf8")
.strip()
)
return comment
def git_commit_id():
commit_id = (
subprocess.check_output("git log -n 1 --format=%H", shell=True)
.decode("ascii")
.strip()
)
return commit_id
@task
def deploy_pip(context):
if "TWINE_PASSWORD" not in os.environ:
raise Exception("Twine token not specified in environment")
subprocess.check_call("twine upload -u __token__ dist/*", shell=True)
@task
def push_pip_commit(context):
import glob
commit_id = git_commit_id()
s3 = boto3.resource("s3")
for g in glob.glob("dist/ai2thor-0+%s*" % commit_id):
acl = "public-read"
pip_name = os.path.basename(g)
logger.info("pushing pip file %s" % g)
with open(g, "rb") as f:
s3.Object(
ai2thor.build.PYPI_S3_BUCKET, os.path.join("ai2thor", pip_name)
).put(Body=f, ACL=acl)
@task
def build_pip_commit(context):
commit_id = git_commit_id()
if os.path.isdir("dist"):
shutil.rmtree("dist")
generate_quality_settings(context)
# must use this form to create valid PEP440 version specifier
version = "0+" + commit_id
with open("ai2thor/_builds.py", "w") as fi:
fi.write("# GENERATED FILE - DO NOT EDIT\n")
fi.write("COMMIT_ID = '%s'\n" % commit_id)
with open("ai2thor/_version.py", "w") as fi:
fi.write("# Copyright Allen Institute for Artificial Intelligence 2021\n")
fi.write("# GENERATED FILE - DO NOT EDIT\n")
fi.write("__version__ = '%s'\n" % (version))
subprocess.check_call("python setup.py clean --all", shell=True)
subprocess.check_call("python setup.py sdist bdist_wheel --universal", shell=True)
@task
def build_pip(context, version):
import xml.etree.ElementTree as ET
import requests
res = requests.get("https://pypi.org/rss/project/ai2thor/releases.xml")
res.raise_for_status()
root = ET.fromstring(res.content)
latest_version = None
for title in root.findall("./channel/item/title"):
latest_version = title.text
break
# make sure that the tag is on this commit
commit_tags = (
subprocess.check_output("git tag --points-at", shell=True)
.decode("ascii")
.strip()
.split("\n")
)
if version not in commit_tags:
raise Exception("tag %s is not on current commit" % version)
commit_id = git_commit_id()
res = requests.get("https://api.github.com/repos/allenai/ai2thor/commits?sha=main")
res.raise_for_status()
if commit_id not in map(lambda c: c["sha"], res.json()):
raise Exception("tag %s is not off the main branch" % version)
if not re.match(r"^[0-9]{1,3}\.+[0-9]{1,3}\.[0-9]{1,3}$", version):
raise Exception("invalid version: %s" % version)
for plat in ai2thor.build.AUTO_BUILD_PLATFORMS:
commit_build = ai2thor.build.Build(plat, commit_id, False)
if not commit_build.exists():
raise Exception("Build does not exist for %s/%s" % (commit_id, plat.name()))
current_maj, current_min, current_sub = list(map(int, latest_version.split(".")))
next_maj, next_min, next_sub = list(map(int, version.split(".")))
if (
(next_maj == current_maj + 1)
or (next_maj == current_maj and next_min == current_min + 1)
or (
next_maj == current_maj
and next_min == current_min
and next_sub >= current_sub + 1
)
):
if os.path.isdir("dist"):
shutil.rmtree("dist")
generate_quality_settings(context)
with open("ai2thor/_builds.py", "w") as fi:
fi.write("# GENERATED FILE - DO NOT EDIT\n")
fi.write("COMMIT_ID = '%s'\n" % commit_id)
with open("ai2thor/_version.py", "w") as fi:
fi.write("# Copyright Allen Institute for Artificial Intelligence 2021\n")
fi.write("# GENERATED FILE - DO NOT EDIT\n")
fi.write("__version__ = '%s'\n" % (version))
subprocess.check_call("python setup.py clean --all", shell=True)
subprocess.check_call(
"python setup.py sdist bdist_wheel --universal", shell=True
)
else:
raise Exception(
"Invalid version increment: new version=%s,current version=%s; must increment the major, minor or patch by only 1"
% (version, latest_version)
)
@task
def fetch_source_textures(context):
import ai2thor.downloader
zip_data = ai2thor.downloader.download(
"http://s3-us-west-2.amazonaws.com/ai2-thor/assets/source-textures.zip",
"source-textures",
"75476d60a05747873f1173ba2e1dbe3686500f63bcde3fc3b010eea45fa58de7",
)
z = zipfile.ZipFile(io.BytesIO(zip_data))
z.extractall(os.getcwd())
def build_log_push(build_info, include_private_scenes):
with open(build_info["log"]) as f:
build_log = f.read() + "\n" + build_info.get("build_exception", "")
build_log_key = "builds/" + build_info["log"]
s3 = boto3.resource("s3")
bucket = ai2thor.build.PUBLIC_S3_BUCKET
acl = "public-read"
if include_private_scenes:
bucket = ai2thor.build.PRIVATE_S3_BUCKET
acl = "private"
s3.Object(bucket, build_log_key).put(
Body=build_log, ACL=acl, ContentType="text/plain"
)
def archive_push(unity_path, build_path, build_dir, build_info, include_private_scenes):
threading.current_thread().success = False
archive_name = os.path.join(unity_path, build_path)
zip_buf = io.BytesIO()
# Unity build is done with CompressWithLz4. Zip with compresslevel=1
# results in smaller builds than Uncompressed Unity + zip comprseslevel=6 (default)
logger.info("building zip archive %s %s" % (archive_name, os.path.join(unity_path, build_dir)))
zipf = zipfile.ZipFile(zip_buf, "w", zipfile.ZIP_DEFLATED, compresslevel=1)
add_files(zipf, os.path.join(unity_path, build_dir), exclude_ext=('.debug',))
zipf.close()
zip_buf.seek(0)
zip_data = zip_buf.read()
logger.info("generated zip archive %s %s" % (archive_name, len(zip_data)))
push_build(archive_name, zip_data, include_private_scenes)
build_log_push(build_info, include_private_scenes)
print("Build successful")
threading.current_thread().success = True
@task
def pre_test(context):
import ai2thor.controller
c = ai2thor.controller.Controller()
os.makedirs("unity/builds/%s" % c.build_name())
shutil.move(
os.path.join("unity", "builds", c.build_name() + ".app"),
"unity/builds/%s" % c.build_name(),
)
def clean():
import scripts.update_private
# a deploy key is used on the build server and an .ssh/config entry has been added
# to point to the deploy key caclled ai2thor-private-github
scripts.update_private.private_repo_url = (
"git@ai2thor-private-github:allenai/ai2thor-private.git"
)
subprocess.check_call("git reset --hard", shell=True)
subprocess.check_call("git clean -f -d -x", shell=True)
shutil.rmtree("unity/builds", ignore_errors=True)
shutil.rmtree(scripts.update_private.private_dir, ignore_errors=True)
scripts.update_private.checkout_branch()
def ci_prune_cache(cache_dir):
entries = {}
for e in os.scandir(cache_dir):
if os.path.isdir(e.path):
mtime = os.stat(e.path).st_mtime
entries[e.path] = mtime
# keeping the most recent 60 entries (this keeps the cache around 300GB-500GB)
sorted_paths = sorted(entries.keys(), key=lambda x: entries[x])[:-60]
for path in sorted_paths:
if os.path.basename(path) != "main":
logger.info("pruning cache directory: %s" % path)
shutil.rmtree(path)
def link_build_cache(root_dir, arch, branch):
library_path = os.path.join(root_dir, "unity", "Library")
logger.info("linking build cache for %s" % branch)
if os.path.lexists(library_path):
os.unlink(library_path)
# this takes takes care of branches with '/' in it
# to avoid implicitly creating directories under the cache dir
encoded_branch = re.sub(r"[^a-zA-Z0-9_\-.]", "_", re.sub("_", "__", branch))
cache_base_dir = os.path.join(os.environ["HOME"], "cache")
os.makedirs(cache_base_dir, exist_ok=True)
ci_prune_cache(cache_base_dir)
main_cache_dir = os.path.join(cache_base_dir, "main", arch)
branch_cache_dir = os.path.join(cache_base_dir, encoded_branch, arch)
# use the main cache as a starting point to avoid
# having to re-import all assets, which can take up to 1 hour
if not os.path.exists(branch_cache_dir) and os.path.exists(main_cache_dir):
logger.info("copying main cache for %s" % encoded_branch)
os.makedirs(os.path.dirname(branch_cache_dir), exist_ok=True)
# -c uses MacOS clonefile
subprocess.check_call(
"cp -a -c %s %s" % (main_cache_dir, branch_cache_dir), shell=True
)
logger.info("copying main cache complete for %s" % encoded_branch)
branch_library_cache_dir = os.path.join(branch_cache_dir, "Library")
os.makedirs(branch_library_cache_dir, exist_ok=True)
os.symlink(branch_library_cache_dir, library_path)
# update atime/mtime to simplify cache pruning
os.utime(os.path.join(cache_base_dir, encoded_branch))
def travis_build(build_id):
import requests
res = requests.get(
"https://api.travis-ci.com/build/%s" % build_id,
headers={
"Accept": "application/json",
"Content-Type": "application/json",
"Travis-API-Version": "3",
},
)
res.raise_for_status()
return res.json()
def pending_travis_build():
import requests
res = requests.get(
"https://api.travis-ci.com/repo/3459357/builds?include=build.id%2Cbuild.commit%2Cbuild.branch%2Cbuild.request%2Cbuild.created_by%2Cbuild.repository&build.state=started&sort_by=started_at:desc",
headers={
"Accept": "application/json",
"Content-Type": "application/json",
"Travis-API-Version": "3",
},
timeout=10,
)
for b in res.json()["builds"]:
tag = None
if b["tag"]:
tag = b["tag"]["name"]
return {
"branch": b["branch"]["name"],
"commit_id": b["commit"]["sha"],
"tag": tag,
"id": b["id"],
}
def pytest_s3_object(commit_id):
s3 = boto3.resource("s3")
pytest_key = "builds/pytest-%s.json" % commit_id
return s3.Object(ai2thor.build.PUBLIC_S3_BUCKET, pytest_key)
@task
def ci_merge_push_pytest_results(context, commit_id):
s3_obj = pytest_s3_object(commit_id)
s3_pytest_url = "http://s3-us-west-2.amazonaws.com/%s/%s" % (
s3_obj.bucket_name,
s3_obj.key,
)
logger.info("pytest url %s" % s3_pytest_url)
merged_result = dict(success=True, stdout="", stderr="")
result_files = ["tmp/pytest_results.json", "tmp/test_utf_results.json"]
for rf in result_files:
with open(rf) as f:
result = json.loads(f.read())
merged_result["success"] &= result["success"]
merged_result["stdout"] += result["stdout"] + "\n"
merged_result["stderr"] += result["stderr"] + "\n"
s3_obj.put(
Body=json.dumps(merged_result), ACL="public-read", ContentType="application/json"
)
def ci_pytest(branch, commit_id):
import requests
logger.info("running pytest for %s %s" % (branch, commit_id))
proc = subprocess.run(
"pytest", shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE
)
result = dict(
success=proc.returncode == 0,
stdout=proc.stdout.decode("ascii"),
stderr=proc.stderr.decode("ascii"),
)
with open("tmp/pytest_results.json", "w") as f:
f.write(json.dumps(result))
logger.info("finished pytest for %s %s" % (branch, commit_id))
@task
def ci_build(context):
lock_f = open(os.path.join(os.environ["HOME"], ".ci-build.lock"), "w")
arch_temp_dirs = dict()
try:
fcntl.flock(lock_f, fcntl.LOCK_EX | fcntl.LOCK_NB)
build = pending_travis_build()
skip_branches = ["vids", "video", "erick/cloudrendering"]
if build and build["branch"] not in skip_branches:
# disabling delete temporarily since it interferes with pip releases
# pytest_s3_object(build["commit_id"]).delete()
logger.info(
"pending build for %s %s" % (build["branch"], build["commit_id"])
)
clean()
subprocess.check_call("git fetch", shell=True)
subprocess.check_call("git checkout %s --" % build["branch"], shell=True)
subprocess.check_call("git checkout -qf %s" % build["commit_id"], shell=True)
private_scene_options = [False]
procs = []
build_archs = ["OSXIntel64", "Linux64"]